added user name to model

chat_qwen.py

@@ -1,8 +1,6 @@
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 from utils.conversation import save_conversation_json
-from utils.prompt import prompt
 
 
 class ChatQwen:
@@ -11,7 +9,9 @@ class ChatQwen:
     default_model_id = 'Qwen/Qwen1.5-1.8B-Chat'
     # default_model_id = 'Qwen/Qwen1.5-4B-Chat'
 
-    default_instruction = {'role': 'system', 'content': 'Your name is "Laura". You are an AI created by Alice.'}
+    default_instruction = {'role': 'system',
+                           'name': 'system',
+                           'content': 'Your name is "Laura". You are an AI created by Alice.'}
 
     def __init__(self, model_id_or_path=default_model_id):
         # model_id = model_id_or_path if not load_from_disk else os.path.abspath(sys.argv[1])
@@ -20,9 +20,9 @@ class ChatQwen:
         self.model_id_or_path = model_id_or_path
         self.model = AutoModelForCausalLM.from_pretrained(model_id_or_path, torch_dtype='auto', device_map='auto')
         self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path)
-        # print(tokenizer.default_chat_template)
-        # print(type(model))
-        # print(type(tokenizer))
+        # print(self.tokenizer.default_chat_template)
+        # print(type(self.model))
+        # print(type(self.tokenizer))
         print('Loaded')
 
     def generate(self, messages):
@@ -41,8 +41,9 @@ class ChatQwen:
             response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
             # add response and save conversation
-            messages.append({'role': 'assistant', 'content': response})
-            self.record_conversation(input_messages, {'role': 'assistant', 'content': response})
+            response_entry = {'role': 'assistant', 'name': 'assistant', 'content': response}
+            messages.append(response_entry)
+            self.record_conversation(input_messages, response_entry)
 
             return messages
         finally: